Panel Data and Diff-in-Diff Chris Hansman Empirical Finance: Methods and Applications January 25-26, 2020 1/76 Some Details First assignment released this week Posted on January 26th Due on February 9th. 2/76 Overview Last class: an introduction to causality This class: estimating causal effects with panel data 1. An introduction to […]
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An Introduction to Causality Chris Hansman Empirical Finance: Methods and Applications Imperial College Business School Week Two January 18, 2021 1/95 Last Week’s Lecture: Two Parts (1) Introduction to the conditional expectation function (CEF) (2) Ordinary Least Squares and the CEF 2/95 Today’s Lecture: Four Parts (1) Analyzing an experiment in R Comparing means
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Candidate Name: CID Number: MSc Risk Management and Financial Engineering Examinations 2018/2019 For internal Students of Imperial College of Science Technology and Medicine. This paper also forms part of the examination for the Associateship. Empirical Finance: Methods and Applications (B (BS1033) Tuesday 12th March; 14:00-16:00 CLOSED BOOK Instructions Only college approved calculators may be used.
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OLS and the Conditional Expectation Function Chris Hansman Empirical Finance: Methods and Applications Imperial College Business School Week One January 11th and 12th, 2021 1/84 This Week Course Details Basic housekeeping Course tools: Menti, R, and R-Studio Introduction to tidy data OLS and the Conditional Expectation Function Review and
Observed Factor Models Chris Hansman Empirical Finance: Methods and Applications Imperial College Business School Topic 5 February 4th 1/76 Today 1. General Framing of Linear Factor Models 2. Single Index Model and the CAPM 3. Multi-Factor Models Fama-French Macroeconomic Factors 4. Barra approach 2/76 Part 1: Linear Factor Models 1. Clarifying the Assumptions
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Prediction and Regularization Chris Hansman Empirical Finance: Methods and Applications Imperial College Business School February 1st and 2nd 1/59 Overview 1. The prediction problem and an example of overfitting 2. The Bias-Variance Tradeoff 3. LASSO and RIDGE 4. Implementing LASSO and RIDGE via glmnet() 2/59 A Basic Prediction Model Suppose y is given by:
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Review Chris Hansman Empirical Finance: Methods and Applications Imperial College Business School March 8-9, 2021 1/102 Topic 1: OLS and the Conditional Expectation Function Consider random variable yi and (variables) Xi Which of the following is false, (a) Xi′βOLS provides the best predictor of yi out of any function of Xi (b) Xi′βOLS
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Principal Components Analysis Chris Hansman Empirical Finance: Methods and Applications Imperial College Business School February 15-16 1/86 Today: Four Parts 1. Geometric Interpretation of Eigenvalues and Eigenvectors 2. Geometric Interpretation of Correlation Matricies 3. An Introduction to PCA 4. An Example of PCA 2/86 Topic 1: Geometry of Eigenvalues and Eigenvectors 1. Technical definitions of
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Limited Dependent Variables Chris Hansman Empirical Finance: Methods and Applications Imperial College Business School February 22-23, 2021 1/76 Today: Four Parts 1. Writing and minimizing functions in R 2. Binary dependent variables 3. Implementing a probit in R via maximum likelihood 4. Censoring and truncation 2/76 Part 1: Simple Functions in R Often valuable
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Euro and Euro Crisis Euro and Euro Crisis Lecture 12 Copyright By PowCoder代写 加微信 powcoder FINC5090 Finance in The Global Economy The University of ‹#› Lecture 12 Euro and euro crisis The European Union and the European Monetary System Theory of optimal currency areas Is the EU an optimal currency area? Other considerations of an
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